Abstract

BACKGROUND:

Neoadjuvant chemotherapy is a key component of breast cancer treatment regimens and pathologic complete response to this therapy varies among patients. This is presumably due to differences in the molecular mechanisms that underlie each tumor's disease pathology. Developing genomic clinical assays that accurately categorize responders from non-responders can provide patients with the most effective therapy for their individual disease.

RESULTS:

In all datasets, E2F4 activity level was an accurate predictor of neoadjuvant chemotherapy response, with high E2F4 scores predictive of achieving pathologic complete response and low scores predictive of residual disease. These results remained significant even after stratifying patients by estrogen receptor (ER) status, tumor stage, and breast cancer molecular subtypes. Compared to the Oncotype DX and MammaPrint signatures, our E2F4 signature achieved similar performance in predicting neoadjuvant chemotherapy response, though all signatures performed better in ER+ tumors compared to ER- ones. The accuracy of our signature was reproducible across datasets and was maintained when refined from a 199-gene signature down to a clinic-friendly 33-gene panel.

CONCLUSION:

Overall, we show that our E2F4 signature is accurate in predicting patient response to neoadjuvant chemotherapy. As this signature is more refined and comparable in performance to other clinically available gene expression assays in the prediction of neoadjuvant chemotherapy response, it should be considered when evaluating potential treatment options.

Percentage of patients achieving pCR in E2F4 activity groups after stratification on clinicopathological characteristics in the Hatzis dataset. a Percentage of patients achieving pCR with low (white), intermediate (grey), and high (dark grey) E2F4 activity groups for all, ER-positive, and ER-negative patients, respectively. b Percentage of patients achieving pCR with low (white), intermediate (grey), and high (dark grey) E2F4 activity groups in patients with different tumor stage. c Percentage of patients achieving pCR with low (white), intermediate (grey), and high (dark grey) E2F4 activity groups in patients belonging to different molecular subtypes. In all panels, horizontal dotted line indicates the percentage of pCR patients without stratifying based on E2F4 activity. P-values were calculated using the χ2 test

Classification performance after including clinicopathological features into pCR classification models. Comparison of AUCs between combinations of the E2F4 signature, Oncotype DX, MammaPrint, and clinicopathological features in a ER-positive patients and b ER-negative patients. Error bars indicate standard deviation calculated by performing 10-fold cross-validation 100 times

Comparison of pCR classification performance between the E2F4 signature, Oncotype DX, and MammaPrint in 4 independent datasets. a Percentage of pCR patients in low (white), intermediate (grey), and high (dark grey) E2F4 activity groups in the Iwamoto (2010), Iwamoto (2011), Tabchy, and Horak datasets. P-values were calculated using the χ2 test. b pCR classification performance using features from the E2F4 signature (black), Oncotype DX (red), and MammaPrint (green) in the Iwamoto (2010), Iwamoto (2011), Tabchy, and Horak datasets. Grey dotted line corresponds to random classification and an AUC of 0.5

Performance of the modified E2F4 score when predicting pCR status in the Hatzis dataset. a Distribution of E2F4 activity scores based on the 33-gene signature. Vertical hashed lines indicate quantile divisions used to denote low (white), intermediate (grey), or high (dark grey) E2F4 activity. b Percentage of patients with pCR or RD that fall within the low (white), intermediate (grey) or high (dark grey) E2F4 score categories. P-value was calculated using the χ2 test. c ROC curves showing pCR classification performance when using the E2F4 scores calculated from the 33-gene signature in all (black), ER-positive (magenta), and ER-negative (aqua) patients. Grey dotted line corresponds to random classification and an AUC of 0.5